High pressure-temperature studies on an olivine tholeiite and a tholeiitic picrite from the pavagarh region, Gujarat, India

Experimental studies have been performed on an olivine tholeiite and tholeiitic picrite at pressure and temperature ranges of 20-40 kb and 1200-1300°C. The lower and upper limits of basalt-eclogite transition zone for tholeiitic picrite are 23 kb and 31.67 kb at 1200°C, and 24.67 kb and 33.67 kb at 1300°C, whereas for olivine tholeiite, these are 27 kb and 32.33 kb at 1200°C, and 28.70 kb and 33.70 kb at 1300°C. While the assemblages for both samples below the transition region are Pl+Px+Mt, they are Pl+Gt+Px+Mt within it. The eclogite field has Gt+Px+Mt. The ratio of garnet to plagioclase increases from the transition zone to the eclogite field and with the disappearance of plagioclase, the percentage of garnet increases to 30 in the eclogite field. Comparison of our results with previous studies on basalt-eclogite transition shows that the transition zone found by us occurs at higher pressure-temperature conditions. Seismic studies of the region below the Deccan Traps show an increase in velocity (1-4%) at depth. It is suggested that after partial melting, during ascent of the basaltic liquid, a significant portion of it crystallizes within the upper mantle as pockets of eclogite. As eclogite is more dense than peridotite, their presence should cause a similar increase in the seismic velocity below the Deccan area

Gangotri glacier dynamics from multi-sensor SAR and optical data

The present study has analyzed dynamics of Gangotri glacier using multiple remote sensing (RS) datasets and ground based observations. Interferometric Synthetic Aperture Radar (InSAR) data pairs from European Remote Sensing satellite (ERS 1/2) tandem pair for spring of 1996, Sentinel-1 SAR pairs and Japanese's Advance Land Observation System (ALOS) PALSAR-2 SAR data for Spring of 2015 were used to derive glacier-surface velocity at seasonal time scale using Differential InSAR (DInSAR) techniques. Bi-static TanDEM-X (Experimental) data was used for the 1st time to estimate glacier surface elevation changes for a period of 22, 44, 88 days during summer of 2012 using InSAR techniques in this study. Annual glacier velocity was also estimated using temporal panchromatic data of LANDSAT-5 (30 m), LANDSAT-7/8 (15 m), Sentinel-2 (10 m) and Indian Remote Sensing Satellite IRS-1C/1D panchromatic (5 m) data during 1998–2019 with feature tracking approach. This study has estimated glacier surface velocity and surface elevation changes for the major parts of Gangotri glacier and its tributary glaciers using medium to high resolution optical and SAR datasets, at annual and seasonal time scale, which is an improvement over earlier studies, wherein snout based glacier recession or only main glacier velocities were reported. The velocity and slope were used to assess glacier-ice thickness distribution using Glabtop-2, slope dependent and laminar flow based methods over the Gangotri group of glaciers. The estimated ice thickness was estimated in the range of 58–550 m for the complete glacier while few small areas in middle &amp; upper regions carry higher thickness of about 607 m. The estimated glacier-ice thickness was found in the range of 58–67 m at the snout region. The estimation was validated using 2014 field measurements from Terrestrial Laser Scanner (TLS) for the first time and correlation was found to be 0.799 at snout of the glacier.</p

Automated identification of potential snow avalanche release areas based on digital elevation models

The identification of snow avalanche release areas is a very difficult task. The release mechanism of snow avalanches depends on many different terrain, meteorological, snowpack and triggering parameters and their interactions, which are very difficult to assess. In many alpine regions such as the Indian Himalaya, nearly no information on avalanche release areas exists mainly due to the very rough and poorly accessible terrain, the vast size of the region and the lack of avalanche records. However avalanche release information is urgently required for numerical simulation of avalanche events to plan mitigation measures, for hazard mapping and to secure important roads. The Rohtang tunnel access road near Manali, Himachal Pradesh, India, is such an example. By far the most reliable way to identify avalanche release areas is using historic avalanche records and field investigations accomplished by avalanche experts in the formation zones. But both methods are not feasible for this area due to the rough terrain, its vast extent and lack of time. Therefore, we develop an operational, easy-to-use automated potential release area (PRA) detection tool in Python/ArcGIS which uses high spatial resolution digital elevation models (DEMs) and forest cover information derived from airborne remote sensing instruments as input. Such instruments can acquire spatially continuous data even over inaccessible terrain and cover large areas. We validate our tool using a database of historic avalanches acquired over 56 yr in the neighborhood of Davos, Switzerland, and apply this method for the avalanche tracks along the Rohtang tunnel access road. This tool, used by avalanche experts, delivers valuable input to identify focus areas for more-detailed investigations on avalanche release areas in remote regions such as the Indian Himalaya and is a precondition for large-scale avalanche hazard mapping

Snowpack Density Retrieval Using Fully Polarimetric TerraSAR-X Data in the Himalayas

This paper focuses on the development of a novel algorithm for deriving snowpack density over the snow-covered region of the Himalayas. The analysis utilizes fully polarimetric TerraSAR-X synthetic aperture radar data sets, field observations, and other ancillary information for the retrieval of snowpack density. The algorithm involves the development of a new generalized hybrid decomposition model. The generalized volume scattering parameter from the decomposition model is inverted for snow density estimation. A few field data measurements' campaigns were carried out, within near-real time of satellite passing over the area, to collect various parameters such as temperature, water content, and the density of the snowpack at varying depths. These field observations are further used for validation of the results obtained from the inversion algorithm. It is also found that the model-estimated snowpack density is highly congruent with the field-measured snowpack density. The mean absolute error of snowpack density, root-mean-square error, and index of agreement are found to be 9.9 kg/m(3), 10 kg/m(3), and 0.96, respectively, which are well within the acceptable range

Estimating surface ice velocity on Chhota Shigri glacier from satellite data using Particle Image Velocimetry (PIV) technique

Information about the surface ice velocity is one of the important parameters for Mass balance and Glacier dynamics. This study estimates the surface ice velocity of Chhota Shigri glacier using Landsat (TM/ETM+) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) temporal data-sets from a period of 2009 to 2016 and 2006 to 2007, respectively. A correlation based Particle Image Velocimetry (PIV) technique has been used for the estimation of surface ice velocity. This technique uses multiple window sizes in the same data-set. Four window sizes (low, medium, high, very high) are used for each image pair. Estimated results have been compared with the published data. The outcomes attained from the medium window size closely matches with the published results. The estimated mean surface ice velocities of medium window size are 24 and 28.5 myr−1 for 2009/2010 and 2006/2007 images pair. Highest velocity is observed in middle part of the glacier while lowest in the accumulation zone of the glacier

GIS-based MCDA–AHP modelling for avalanche susceptibility mapping of Nubra valley region, Indian Himalaya

Avalanches are behind the majority of fatalities and heavy damage to property in snow-covered mountainous terrain like Himalaya. Recognizing avalanche susceptible areas and publication of avalanche susceptibility maps assist decision-makers and planners to execute suitable measures to reduce the avalanche risk. The present study is an attempt to prepare an avalanche susceptibility map of the Nubra valley region using multi-criteria decision analysis–analytical hierarchy process model in GIS environment. The most prominent avalanche occurrence factors used in this model are slope, aspect, curvature, elevation, terrain roughness and ground cover. ASTER GDEM V2 and Landsat 8 satellite imagery were used to generate considered factors. For validation of the results, prediction rate/accuracy is calculated using the avalanche inventory map of documented avalanche locations. To calculate the prediction accuracy, area under the ROC curve (ROC-AUC) method has been used. The prediction accuracy of the validation results using ROC-AUC shows 91%

Snow Cover Mapping Using Polarization Fraction Variation With Temporal RADARSAT-2 C-Band Full-Polarimetric SAR Data Over the Indian Himalayas

Remote sensing is an indispensable tool for Earth observation over large areal extent. Snow cover extent monitoring has been one such application where microwave sensors have been a popular choice due to their sensitivity toward the dielectric property. Snow is a dynamic matter since its dielectric state is dependent on climatic factors prevailing locally around it. In the literature, the polarization fraction (PF) has been used for landcover characterization. In this paper, we utilize the seasonal variation of the PF for mapping snow cover over the Himalayan terrain. Temporal variation of the strength in the polarized return due to change in landcover and season is the prime motivation behind this approach. In addition, the effect of SAR data acquisition time on mapping algorithms is considered in this work which is seldom discussed in the literature. Furthermore, a study is conducted to analyze the seasonal variation in the entropy (H) and the scattering type (a) parameters over the bare snow-covered ground and the forested areas. The seasonal response of the terrain in the H/a plane corresponding to specific scattering characteristics is analyzed. The applicability of the approach is tested with RADARSAT-2 (FQ28) C-band full polarimetric image pairs over the Manali-Dhundi region, Himachal Pradesh, India, located in the western Hindu-Kush Himalayas. The snow cover map is explicitly validated with in situ observatory measurements and optical satellite imagery

SAR interferometry for DEM generation and movemnet of Indian glaciers

Two famous glaciers viz. Gangotri and Siachen were studied for DEM generation and movement usiny ERS-1&2 tandem data. While surrounding areas along the glaciers showed more decorrelation, glacier area showed a very good correlation between two image acquisitions. Contours obtained using ERS-1&2 SAR tandem data closely match with topographic maps of the area. Two-pass differential method was used along with SRTM DEM to study the movement of the glaciers. According to the differential interferogram over Gangotri, 3 fringes equivalent to 8.4 em displacement in the radar look direction were observed, whereas for Siachen the fringes were about 8 which is equivalent to. 22 cm. The estimated DEMs and movements are to be verified using GPS measurements

Snowpack Density Retrieval Using Fully Polarimetric TerraSAR-X Data in the Himalayas

This paper focuses on the development of a novel algorithm for deriving snowpack density over the snow-covered region of the Himalayas. The analysis utilizes fully polarimetric TerraSAR-X synthetic aperture radar data sets, field observations, and other ancillary information for the retrieval of snowpack density. The algorithm involves the development of a new generalized hybrid decomposition model. The generalized volume scattering parameter from the decomposition model is inverted for snow density estimation. A few field data measurements' campaigns were carried out, within near-real time of satellite passing over the area, to collect various parameters such as temperature, water content, and the density of the snowpack at varying depths. These field observations are further used for validation of the results obtained from the inversion algorithm. It is also found that the model-estimated snowpack density is highly congruent with the field-measured snowpack density. The mean absolute error of snowpack density, root-mean-square error, and index of agreement are found to be 9.9 kg/m(3), 10 kg/m(3), and 0.96, respectively, which are well within the acceptable range